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https://ceur-ws.org/Vol-3141/preface.pdf
Preface for the 3rd Edition of the International
Knowledge Graph Construction Workshop
David Chaves-Fraga1,2,3 , Anastasia Dimou1,3,4 , Pieter Heyvaert5 , Freddy Priyatna6 and
Juan Sequeda7
1
KU Leuven, Department of Computer Science, Sint-Katelijne-Waver, Belgium
2
Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, Spain
3
Flanders Make – DTAI-FET
4
Leuven.AI – KU Leuven institute for AI, B-3000 Leuven, Belgium
5
IDLab, Dept of Electronics and Information Systems, Ghent University – imec, Belgium
6
Olive AI, EEUU
7
data.world, EEUU
More and more knowledge graphs are constructed for private use, e.g., the Amazon Prod-
uct Graph [1] or the Fashion Knowledge Graph by Zalando1 ,or public use, e.g., DBpedia2 or
Wikidata3 . While techniques to automatically construct KGs from existing Web objects exist
(e.g., scraping Web tables), there is still room for improvement. So far, constructing knowledge
graphs was considered an engineering task, however, more scientifically robust methods keep
on emerging. These methods were widely questioned for their verbosity, low performance
or difficulty of use, while the data sources’ variety and complexity cause further syntax and
semantic interoperability issues.
Declarative methods (mapping languages) for describing rules to construct knowledge graphs
and approaches to execute those rules keep on emerging. Nevertheless constructing knowledge
graphs is still not a straightforward task because several existing challenges remain and yet
the barriers to construct knowledge graphs are not lowered enough to be easily and broadly
adopted by industry. These reasons and the vastly populated knowledge graph construction W3C
Community Group4 show that there are still open questions that require further investigation
to come up with groundbreaking solutions.
Addressing challenges related to knowledge graphs construction requires well-founded
research, including the investigation of concepts and development of tools as well as methods
for their evaluation. R2RML was recommended in 2012 by W3C, and since then, different
extensions, alternatives and implementations were proposed [2, 3, 4]. Certain approaches
followed the ETL-like paradigm, e.g., SDM-RDFizer [5], RocketRML [6], and FunMap [7], while
Third International Workshop On Knowledge Graph Construction Co-located with the ESWC 2022, 30th May 2022, Crete,
Greece
Envelope-Open david.chaves@upm.es (D. Chaves-Fraga); anastasia.dimou@kuleuven.be (A. Dimou); pieter.heyvaert@ugent.be
(P. Heyvaert); freddy.priyatna@oliveai.com (F. Priyatna); juan@data.world (J. Sequeda)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://engineering.zalando.com/posts/2018/03/semantic-web-technologies.html
2
https://www.dbpedia.org/resources/knowledge-graphs/
3
https://www.wikidata.org/wiki/Wikidata:Main_Page
4
http://w3.org/community/kg-construct
others the query-answering paradigm, e.g., Ultrawrap [8], Morph-RDB [9] and Ontop [10].
Besides R2RML-based extensions, alternatives were proposed, e.g., SPARQL-Generate [11] and
ShExML [12], as well as methods to perform data transformations while constructing knowledge
graphs, e.g., FnO [13] and FunUL [14].
The third edition of the knowledge graph construction workshop5 has a special focus on
the automatization of knowledge graph construction methods, analyzing their alignment with
previous standard but declarative approaches using mapping rules. It also included:
• Keynote. The workshop includes the keynote from Javier D. Fernandez and Selena Baset
(Roche): “From ETL to DIY, or how to democratize the creation of Knowledge Graphs”
• SemTab challenge 2022.6 : Kick-off for the Semantic Web Challenge on Tabular Data to
Knowledge Graph Matching for 2022.
The final goal of the event is to provide a venue for scientific discourse, systematic analysis
and rigorous evaluation of languages, techniques and tools, as well as practical and applied
experiences and lessons-learned for constructing knowledge graphs from academia and industry.
Eleven papers were submitted. The reviews were open and public, and hosted at Open
Review7 . Each paper received at least three reviews from reviewers with different background
and status. Each paper received a review from a senior, a junior and an industry researcher.
Six papers were accepted and one was conditionally accepted. two of the accepted papers
were long papers and five were short papers. The following papers were accepted for publication
and presented at the workshop:
• Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in
Social Networks [15]
• A Human-in-the-Loop Approach for Personal Knowledge Graph Construction from File
Names [16]
• Continuous generation of versioned collection’s members with RML and LDES [17]
• Implementation-independent Knowledge Graph Construction Workflows using FnO
Composition [18]
• Declarative Description of Knowledge Graphs Construction Automation: Status & Chal-
lenges [19]
• Devising Mapping Interoperability with Mapping Translation [20]
• Supporting Relational Database Joins for Generating Literals in R2RML [21]
Organizing Committee
• David Chaves-Fraga, Universidad Politécnica de Madrid & KU Leuven
• Anastasia Dimou, KU Leuven, Flanders Make, Leuven.AI
• Pieter Heyvaert, Ghent University - imec
• Freddy Priyatna, Olive AI
• Juan Sequeda, data.world
5
http://w3id.org/kg-construct/workshop/2022
6
https://www.cs.ox.ac.uk/isg/challenges/sem-tab/
7
https://openreview.net/group?id=kg-construct.github.io/KGCW/2022/Workshop
Program Committee
• Aidan Hogan, Universidad de Chile
• Ana Iglesias-Molina, Universidad Politécnica de Madrid
• Antoine Zimmermann, École des Mines de Saint-Étienne
• Ben De Meester, Ghent University – imec
• Boris Villazón-Terrazas, Tinámica
• Christophe Debruyne, Liège University
• Dylan Van Assche, Ghent University – imec
• Edna Ruckhaus, Universidad Politécnica de Madrid
• Elvira Amador, Universidad Politécnica de Madrid
• Ernesto Jiménez-Ruiz, University of London
• Femke Ongenae, Ghent University – imec
• Franck Michel, Université Côte d’Azur
• François Scharffe, Columbia University
• Giorgos Flouris, FORTH
• Giuseppe Futia, Nexa Center
• Hannes Voigt, Neo4j
• Heiko Paulheim, University of Mannheim
• Herminio Garcia Gonzalez, Universidad de Oviedo
• Jakub Klímek, Charles University
• Julián Arenas-Guerrero, Universidad Politécnica de Madrid
• Jhon Toledo, Universidad Politécnica de Madrid
• Manolis Koubarakis, National & Kapodistrian University of Athens
• Maria-Esther Vidal, L3S & TIB
• Mario Scrocca, CEFRIEL
• Mauro Dragoni, FBKZ
• Maxime Lefrancois, École des Mines de Saint-Étienne
• Miel Vander Sande, Memoo
• Mohamed Nadjib Mami, Deutsche Post DHL Group
• Nora Abdelmageed, Friedrich-Schiller-University Jena
• Oscar Corcho, Universidad Politécnica de Madrid
• Pano Maria, Skemu
• Samaneh Jozashoori, L3S & TIB
• Semih Salihoglu, University of Waterloo
• Sergio José Rodríguez Méndez, Australian National University
• Souripriya Das, Oracle
• Sven Lieber, Royal Library of Belgium (KBR)
• Umutcan Şimşek, University of Innsbruck
• Vladimir Alexiev, Ontotext
Acknowledgments
This publication is based upon work from COST Action Distributed Knowledge Graphs, sup-
ported by COST (European Cooperation in Science and Technology). COST (European Cooper-
ation in Science and Technology) is a funding agency for research and innovation networks.
Our Actions help connect research initiatives across Europe and enable scientists to grow their
ideas by sharing them with their peers. This boosts their research, career and innovation.
http://www.cost.eu/
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